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Record W2999646328 · doi:10.1126/scitranslmed.aax9340

Stromal cell protein kinase C-β inhibition enhances chemosensitivity in B cell malignancies and overcomes drug resistance

2020· article· en· W2999646328 on OpenAlexaff
Eugene Park, Jingyu Chen, A. P. Moore, Maurizio Mangolini, Antonella Santoro, Joseph R. Boyd, Hilde Schjerven, Veronika Ecker, Maike Buchner, James C. Williamson, Paul J. Lehner, Luca Gasparoli, Owen Williams, Johannes Bloehdorn, Stephan Stilgenbauer, Michael Leitges, Alexander Egle, Marc Schmidt‐Supprian, Seth Frietze, Ingo Ringshausen

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMemorial University of Newfoundland
FundersMedical Research CouncilAustrian Science FundDeutsche ForschungsgemeinschaftCHILDREN with CANCER UKNational Cancer InstituteCancer Research UKParacelsus Medizinische PrivatuniversitätWellcome Trust
KeywordsStromal cellCancer researchDrug resistanceProtein kinase ACellChemosensitivity assayDrugKinaseChemistryMedicineBiologyPharmacologyCell biologyTumor cellsBiochemistryGenetics

Abstract

fetched live from OpenAlex

) in tumor cells. Central to the stroma-mediated drug resistance is the PKC-β-dependent activation of transcription factor EB, regulating lysosome biogenesis and plasma membrane integrity. Stroma-directed therapies, enabled by direct inhibition of PKC-β, enhance the effectiveness of many antileukemic therapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.276
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2020
Admission routes1
Has abstractyes

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